Method for providing access to online employment information

ABSTRACT

The present invention provides a method of managing employment data so as to provide access to the employment data via the Internet ( 18 ). The method including the steps of determining whether a web site ( 22, 24 ) contains employment data, formatting, parsing and storing the employment data and corresponding URL into a database, automatically searching the database ( 16 ) for matching employment data, and contacting the employer representative as to the matched employment data.

FIELD OF INVENTION

The present invention relates to employment services and, in particular,to online recruiting or employment services.

BACKGROUND OF THE INVENTION

The rapid expansion of job postings on the Internet has created a largeamount of employment related information, which spans hundreds ofthousands of web sites. Initially, companies began posting their openjob positions on their own corporate web sites. A job seeker could thenreadily access new employment opportunities by visiting a company's website. As an increasing number of company web sites began to post theiropen jobs, however, the job search process grew proportionally. Forexample, a job seeker searching for a “software developer” positionwould have had to identify and visit the web site of every company thatmight have such open job positions. Thus, this growth resulted in a taskthat was cumbersome and time consuming for the job seeker.

In order to help address these issues, job board web sites have evolvedon the Internet. The original purpose of a job board was to provide asingle web site where companies could visit to post their open jobpositions and job seekers could visit to search for new employmentopportunities. The job board concept helped the job seekers by creatinga central location that a job seeker could visit to search for jobs.

Unfortunately, however, the concept increased the work and cost forcompanies. In addition to maintaining job postings on their owncorporate web sites, companies were now required to visit the job boardsites to repost, update and delete their job position information asappropriate. The accuracy of the job board information was affected whencompanies changed their job information, filled open position, etc., butfailed to update the corresponding job board postings. These job boardsalso often charged a fee to the companies for this posting service. Inaddition, these job boards only contained job positions from companiesthat had actively posted jobs on the sites. In other words, companiesthat did not know about the job boards would have been prevented fromlisting the company's open positions and, consequently, eliminatedopportunities for the job seekers as well as the company itself.

Most recently, the aggregation, accuracy, and freshness of job boardpostings have been addressed through various web spidering or crawlingtechnologies. The technology of web site spidering or crawling consistsof a process in which content from a set of source web sites isretrieved automatically. This content is typically retrieved for purposeof being indexed into a search engine web site in order to provideInternet users a central web site to use as a search tool. The type ofcontent that is spidered is generally not filtered so the search engineweb site often has indexed content from a wide variety of source websites. New web sites that contain content to be spidered have toregister with the search engine web site before their content isretrieved and indexed into the search engine. Once a new site isregistered into the set of source web sites to spider, the search engineweb site will periodically spider the site to search for new or updatedcontent to index.

In these updated models, the job board periodically sends out spiders tothe web sites of companies that register with the job board web site.The purpose of these spiders is to retrieve and input the latest jobposting information from the company web sites and thereby automaticallyupdate the job information listed on the job board. The method, however,creates a disadvantage for companies and job seekers because the sitesdo not post the numerous job positions from the companies that do notregister with or know of the job board web site. As such, the Internetcontains a vast amount of job postings which exist only on company jobboards and which are not being collected and displayed by the job boardweb sites.

Another new approach to job posting aggregation is the master searchengine site. In this approach, the master web site collects a jobseelcer's search criteria and submits it to multiple other job board websites. The master search engine site aggregates the individual sites andpresents the results to the job seeker in a single format. An advantageto this method is that the job seeker only needs to visit a single siteto perform a job search. The disadvantages of this approach are that, asdescribed above, only a subset of the job board sites on the Internetare actually searched and individual company job postings are completelyomitted. Furthermore, in these types of searches, the formatting of theresults can vary thereby causing the job seeker to become confused whenpresented with search results.

An additional feature of prior art job board web sites is the electronicnotification of new job opportunities. When a new job is posted thatfits within his selected category information, the job seekerautomatically receives notification of the new job via email. Alimitation to this system is that user may miss employment opportunitieswhich are filtered outside of the selected category information.

Another drawback of the prior art systems relate to the search enginesused for identifying a position of interest to the job seeker. The priorart systems use a table, key word or boolean driven search engine. Thesearch engines use a pull-down menu, keyword or boolean searchmethodology that has a limited ability to implement intelligentsearches. For instance, a job seeker may be in search of a position in aspecific technical field. A search of job postings with one or twokeywords may identify many unrelated jobs. It may be very time consumingfor the job seeker to review every identified job posting. The effortbecomes even greater when compounded by the number of such searches tobe completed at each of the numerous online employment sites. The jobseeker may use additional keywords to reduce the number of unrelated jobpostings. However, the additional keywords often have the effect ofreducing certain of the job postings, which may be of interest to thejob seeker, but do not necessarily contain all of the designatedkeywords. In other words, the search strategy may have become toorestrictive. Therefore, the job seeker ends up accessing only a smallfraction of jobs currently available on the Internet.

Along with the evolution of job board related web sites, the prior artsystems have provided job seekers the ability to post electronicallytheir resumes. These systems have increased the amount of resumesavailable online. This increase has created web sites, which collectresumes into searchable databases. These web sites often sellsubscription access to their databases, which employers and recruiterspurchase in order to search for qualified candidates. However, these websites suffer from the same disadvantages and limitations as described inthe job posting process: a) companies and job seekers must visit the websites to add and update information; b) searches are limited to narrowlytargeted keywords; and c) job seeker resumes are sorted into restrictivecategories.

Furthermore, if these companies do not post at the job board web sites,without adequate traffic to their corporate web site and employmentpages, employers cannot, on their own, reach a sufficient number ofqualified candidates. As a result, the employers must choose to eitherpay the third party job board web sites to post a portion of their jobsonline, making these opportunities accessible to a larger candidatepool, or miss many qualified candidate. Despite this investment,however, the factors listed above still limit the effectiveness of thejob boards and prevent many qualified candidates from matching with theopportunities employers have paid to list.

In summary, there are deficiencies in the current state of the art inthe Internet based employment process. The gap between job boardlistings and actual online jobs is growing rapidly. Companies developand add recruiting pages to their own web sites much faster than therate at which the top job boards add clients. Moreover, the gap betweenunique job board listings and unique jobs available online is expandingat an even faster pace, as companies that use job boards often post thesame opening to between six and ten sites. Furthermore, the current website job boards fail to aggregate completely all job postings on theInternet. Even the sites that aggregate a larger amount of the availablejob listings are limited by the search engine technology currently usedby those job boards. In addition, the current prior art systems aredeficient in their information exchange capabilities. Job board websites rely on companies and/or job seekers to continually visit the jobboard web sites and update the applicable information.

SUMMARY OF THE INVENTION

The object of the present invention is a method of managing employmentdata to provide enhanced access via the Internet to the employment data.

A further object of the present invention is to provide a more thoroughand precise searching of the employment data.

Still a further object of the present invention is to updateautomatically the employment data collected by the present invention.

Still yet a further object of the present invention is to format theemployment data so as to allow for a more accurate and efficient searchof the employment data.

Still yet a further object of the present invention is to matchautomatically users to fulfill employment needs.

In general, the present invention consists of several key subsystems.These subsystems are based on existing software technology, informationspidering and concept based searching, which is new in its applicationto the Internet related employment industry.

The present invention builds on the technology of job spidering andaggregation and incorporates it into the employment field. For example,the working set of web sites which this system spiders includes theentire Internet directory (“Dot Com database”). Thus, both companies andjob boards are included in the job posting collection. Furthermore, theuse of spidering technology is extended to resume collection as well asspidering of job postings. This allows the creation of a much morecomprehensive and complete database of the available employment data.

The present invention also applies a concept based search engine to theemployment search and match problem. As noted above, prior art searchengine web sites are commonly based on keyword search engine technology.In its simplest form, a keyword search takes a set of comma delimiteduser input words and scans its document set for one or more word orpartial word matches. Keyword searches, however, have been enhanced toinclude word count statistics, i.e., how often a word appears in adocument increases its relevancy, and boolean operators, i.e., a usercan search for specific terms to return documents that must contain bothwords. Unfortunately, these searches remain as simple word patternmatching technology, and the casual Internet user does not necessarilypossess a clear understanding of query word relevancy or boolean logic.

In order to improve the user search experience, concept based searchengines were created. The premise of a concept based search engine isthat it is able to “learn” thematic information regarding the documentsthat it indexes. This learning is typically accomplished by applyingBayesian reasoning and neural network technology to each document whenit is indexed. Users are often able to search the database by using fullsentence, natural language queries instead of keyword sets and booleanlogic. As a concept based search engine learns its document set, it canalso make distinctions and relations. This learned information allows auser to search effectively for information without knowing exactly whatis being sought or how the query should be phrased.

Another important feature of a concept based search engine is that theuser will always be provided with some form of results. The results fromsuch a search engine are typically returned in descending weight order.A result with 100% weight is highly relevant to the user's query, whilea result with 1% weight contains little or no relevance to the search.This behavior is a key feature of the concept based search engine,because it allows a programmatic decision to be made based on the“goodness” of a particular result.

The use of a concept based search engine in the present inventioneliminates the need for the user to categorize a job posting or resumeinto a fixed category list and to rely on simple keyword based searchesto find information, thereby providing an accurate and thorough searchresult. The present invention then automatically spiders job and resumerelated web sites for content, indexes the content into its conceptbased search engines, matches the content between jobs and resumes, andnotifies companies and job seekers of new mutual opportunities. Thisprocess occurs continuously to maximize the timeliness and freshness ofthe information exchange.

Also, the present invention is able to accept a wide range of jobposting formats and resume formats. The format of a job posting orresume will vary, often significantly, from web site to web site and jobseeker to job seeker. By enhancing the process with newly developedsoftware, which targets the online employment information, the system isable to index this diverse data into a common format. Once in a commonformat, matches within the data between job postings and resumes areefficiently performed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram of the system of the presentinvention.

FIG. 2 shows a functional flowchart for creating and accessing adatabase of employment data available on the Internet.

FIG. 3 shows a flow chart for determining if the visited web sites meetthe employment criteria.

FIG. 4 shows a flow chart for updating automatically the employment datastored in the database.

FIG. 5 shows a flow chart for formatting and parsing the employmentdata.

FIG. 6 shows a flow chart for adjusting the revisitation period of thevisited web sites.

FIG. 7 shows a flowchart showing the aging and deletion step.

FIG. 8 shows a flow chart for collecting subscriber search criteria andconducting a concept-based search using the criteria.

FIG. 9 shows a flowchart of matching the employment data and notifyingthe users.

FIG. 10 provides a table depicting employment data.

DETAILED DESCRIPTION OF THE INVENTION

With reference to FIG. 1, a system 10 of managing employment data isshown. The system 10 includes a dedicated spidering server 12, adedicated search, retrieve and process server 14 and a database 16. Thesystem 10 provides users (not shown) with the ability to search, via theInternet 18, for employment data located at public job boards 20,corporate web sites 22 and other web sites 24. Users are provided accessto the system 10 via user Internet connections 26. The Internetconnections 26 may be personal computers, for example.

The dedicated spidering server 12 is used to search the Internet for theemployment data. FIG. 10 provides a table showing an example ofemployment data 28 or information available via the Internet 18. Oncethe employment data is located, relevant information is loaded into thedatabase 16. The dedicated search, retrieve and process server 14provides the user the ability to search the database 16 for employmentdata. Users include corporation representatives seeking to fill aposition, agents working for the corporations, as well as individualsseeking an employment position. The process server 14 also conductsautomatic searches of the database for matching employment data (i.e.,matching jobs and resumes).

It will become clear from FIG. 2 that the database 16 of FIG. 1represents multiple databases having individual functions. FIG. 2discloses a process or functional block diagram of the presentinvention. In particular, FIG. 2 discloses a process which dynamicallyretrieves and indexes large amounts of web employment data and processesthis information in an efficient and timely manner. The Dot Corndatabase 30 contains a listing of all the active domain names on theInternet 18. The prequalify dictionary 32 consists of a concept basedsearch engine that has been loaded with template documents to identifyweb pages that contain job posting or resume information. The siteprequalification step 34 receives input from the Dot Corn database 30and the prequalify dictionary 32. The site prequalification step 34filters web sites that contain job postings or resumes. The output ofstep 34 includes URL records, which are stored in the active spider'sdatabase 36. Step 34 is shown in greater detail in FIG. 3. Step 3.2 ofFIG. 3 begins with reading the prequalify dictionary 32. Step 3.3 readsthe next record from the Dot Corn data base 30. Step 3.5 consists ofdetermining whether the record is scheduled for a check. At step 3.6,each record is checked against the Internet domain named service (DNS)to verify whether an active web site exists for the domain name. In theevent it is determined that an active web site does not exist, then step3.13 consists of scheduling the web site or record for a future check.In the event the web site is active, step 3.8 consists of fetching thecontent of the web site. Step 3.10 consists of checking the site contentagainst the prequalify dictionary 32. The prequalify dictionary 32contains a concept base search engine which has been configured withtemplate sample documents of job postings and resumes. Each page of sitecontent that is retrieved at step 3.8 is presented as a query input tothe prequalify dictionary concept based search engine at step 3.10. Thesearch engine returns a rated percent result, which indicates howrelevant a particular site page is with respect to job postings orresumes. If a web site is determined to contain documents of sufficientrelevancy, the site is stored in the active spider's database 36,enabling the site to be regularly spidered for its content. The retrievecontent is stored in the spidered content database 38. If a web sitedoes not exist or has no relevant content, it is scheduled at step 3.13for a future check, at which time the site prequalification step 34 willrevisit the site to repeat the foregoing process.

The site prequalification step 34 contains several key operatingparameters, including the maximum number of pages to retrieve from asingle web site, the amount of time to spend spidering a single web siteand a threshold relevancy wait that is used to indicate whether the sitecontains job postings or resumes of related content. Critical to thisstep is the configuration of the prequalify dictionary 32, as itsdocument set is the mechanism that controls which web sites are acceptedas valid and which are rejected. The architecture of a site groupprequalification step 34 is readily scalable, as in practice severalservices can be operating in parallel on the Dot Corn data base 30 toperform the web site validation process. By scaling services in thismanner, the information scan rate of the millions of records of the DotCorn database 30 is easily controlled.

The periodic spidering step 40 of FIG. 2 is responsible for running eachof the spiders in the active spider's database 36 on a regular,scheduled basis. FIG. 4 discloses the periodic spidering step 40 ingreater detail. Step 4.2 consists of reading the next record from theactive spider's database 36. Step 4.4 determines whether the web sitecorresponding to the record is scheduled to be spidered. In the eventthe web site is scheduled to be spidered, step 4.5 fetches the sitecontent. Step 4.7 determines whether the newly fetched content haschanged from the corresponding content previously stored in the spideredcontent database 38 (FIG. 2) to determine whether the web site haschanged. If a change has occurred, the new content is stored in thespider content database 38 for further processing.

If it is determined at step 4.6 that the spider fails when accessing aparticular web site, step 4.9 consists of identifying the site as“failed” and removing the sit& from the active spider's database 36.Step 4.10 updates the Dot Corn database 30 to schedule the site to berequalified at a later time.

Step 40 is designed to run continuously to ensure that when the contentof each source site changes, it is quickly updated in the spider contentdatabase 38. Thus, the timeliness and freshness of the information ispreserved. Step 40 is readily scalable, as in practice several servicescan be operated and parallel to perform this spidering process. Asadditional spiders are created, additional service can be added tohandle the new load.

The content processing step 42 of FIG. 2 consists of further processingthe content, which is temporarily stored in the spider content database38. The processing dictionary 44 consists of a concept based searchengine, which is similar to the prequalify dictionary 32. The searchengine has been loaded with additional template documents that enablespidered content to be parsed and scrubbed prior to being loaded intothe searchable content database 46. The content processing step 42 isshown in greater detail in FIG. 5. The content processing step 42 isresponsible for processing each retrieved document into a format that issuitable for indexing into the searchable content database 36. Theprocessing dictionary 44 contains a concept based search engine, whichhas been configured with documents that contain specific job titles, jobdescriptions and resume descriptions. The dictionary 44 is used tomeasure the relevance of each spidered content document to determinewhether it should be classified as a job-posting, resume or irrelevant,at which time the content is discarded. Another task of step 42 is theparsing and analysis of web pages, which contain multiple sets ofinformation. For example, a single web page, which contains 15 differentjob postings, is broken down into 15 separate documents utilizingavailable advanced document parsing technology. Each document wouldcontain its own title and specific job location information. Theimproved content results in a search experience that is clear andconcise to the user.

Step 5.2 consists of reading the processing dictionary 44. Step 5.3consists of reading the next record from the spidered content data base38. Step 5.5 strips the document of its hypertext markup language (HTML)commands. The stripped document is evaluated by step 5.6 for its lengthrequirements, and is scanned at step 5.7 and 5.8 to identify thelocation information (city, state, and zip code), and the e-mail addressinformation.

The document is then presented as query input through the processingdictionary 44. The concept based search engine is used to furtheridentify the document as a job posting or resume as well as determineits title information and amount of different information which thedocument may contain (see step 5.9). Documents that do not meet minimumrelevancy requirements as a job posting or resume are discarded (step5.10 and 5.12). Documents that pass the noted criteria are indexed intothe searchable content database 46 as a job posting or resume (step5.13).

After a document passes through this process, its record in thesearchable content database 46 represents a uniform entry, which isconsistent with the other records. The content processing step 42 isdesigned to run continuously as new information is placed into thespidered content database 38. Thus, the timeliness and freshness of theinformation is preserved. Step 42 is readily scalable, as in practiceseveral servers can be operating in parallel to perform the contentprocessing. As the input spidering process information flow increases,additional servers can be added to handle the new content processingload.

The spider adaptation step 48 of FIG. 2 is responsible for dynamicallyadjusting the operating parameters of each spider. The adaptation step48 is shown in greater detail in FIG. 6. Step 6.2 consists of readingthe next site of which the content was previously processed and storedin the searchable content database 46. In the event it is determined atstep 6.4 that the particular spider failed or retrieved irrelevantcontent (not job posting or resume related content), then step 6.10 setsthe spider status as “failed” in the active spider data base 36, and atstep 6.11, the Dot Corn data base 30 is updated to requalify the failedsite at a later time.

Step 6.5 compares the content retrieved at step 6.2 with the contentpreviously stored in the searchable content database 46. Step 6.6determines whether the changed limit has been exceeded. Based on theamount of changes that have occurred, the spider schedule will beadjusted accordingly. In the event the change limit has been exceeded,then step 6.12 will set the spider to run again the following day. Inthe event the change limit has not exceeded, then step 6.7 and 6.8 willincrease the spider frequency for that particular site by an additionalday if the delay is presently less than 30 days. The spider adaptationstep 48 is designed to run continuously as a feedback loop between thecontent processing step 42 and the periodic spidering step 40. Step 48is readily scalable, as in practice several servers can be operating inparallel to perform this step 48. As the input spidering processinformation flow increases, additional service can be added to handlethe new load.

The aging and deletion step 50 is responsible for expiring oldinformation in the searchable content database 46. The aging anddeletion step 50 is shown in greater detail in FIG. 7. Step 7.2 readsthe next record from the searchable content data base 46. Step 7.4determines whether the document date has expired. In the event thedocument date has expired, step 7.5 deletes the document from thesearchable content database 46. Step 50 ensures that old web sites thathave been removed from the Internet are identified, and their contentdocument sets are purged from the overall system. The aging and deletionstep 50 is designed to run continuously, and it is readily scalable, asin practice several servers can be operating in parallel to perform thisaging and deletion step. As the input spidering process information flowincreases, additional servers can be added to handle the new load.

The result of the foregoing provides a searchable content database 46 ofjob positions and resumes, which may be “manually” searched by users aswell as searched via an automatic process.

The “manual” search is initiated at the user search step 52 andcontinues with the concept phase step 54, the keyword phase step 56 andconcludes with the search results 58. FIG. 8 discloses additionaldetails as to the user search. Step 8.2 consists of reading the usersearch input. Step 8.3 determines whether the title, description or keywords have been entered. However, the user may further includeinformation such as the city, state, range of location and number ofresults returned, etc. The concept phase step 54 occurs at step 8.6whereupon concept searching is conducted upon the searchable contentdatabase 46 using the user input. The results are processed at step 8.8whereupon traditional text processes and techniques are used on theresult to produce a filtered result set. Step 8.9 determines whether thequantity of the results meets the users specified quantity in order todetermine whether the search may be concluded.

The user search step provides a front-end, manual interface for jobseekers and employers or recruiters to search for employment data, i.e.,job postings or resumes, respectively. The job seeker's search isprovided as a free service, whereas the resume search is sold as asubscription service.

The user search is designed to run on user demand, and is readilyscalable, as in practice several servers can be operating in parallel toservice multiple user search requests. As the number of new userssearching the system increases, additional servers can be added tohandle the new load.

The automatic match step 60 is responsible for identifying matchesbetween the employer's (job postings) and job seekers (resumes). Asmatches are identified, both the employer and job seeker are notifiedvia e-mail. FIG. 9 discloses the automatic match step 60 in greaterdetail.

Step 9.2 consists of reading the next new job posting from thesearchable content database 46. Step 9.4 consists of using the contentsof the new job posting as query input to perform a concept based searchon the resumes in the searchable content data base 46. The results ofthis search consist of a set of resumes that meet a relevant percentrate with respect to the job posting content. The candidates of theseresumes are identified as “good matches” for a particular job posting.At steps 9.6 and 9.7, the employer corresponding to the new job postingand the candidates corresponding to the identified resumes, arecontacted via e-mail.

Step 9.8 consists of reading the next new resume from the searchablecontent data base 46. At step 9.10, the contents of the new resume areused as query input to perform a concept based search on the jobpostings in the searchable content database 46. The results of thissearch consist of a set of job postings that meet a relevant percentrate with respect to the resume content. The jobs are identified as“good matches” for the particular candidate. Steps 9.12 and 9.13 consistof contacting the employers corresponding to the job posting results,and the candidate corresponding to the new resume.

When a candidate receives an e-mail message containing the jobdescription(s), the candidate is able to access the job posting details,company information, etc. free of charge. Once the candidate reviewsthis information, the candidate may choose to apply to a job, also freeof charge. When an employer or recruiter receives the e-mail messageidentifying an eligible candidate(s) and the qualification summaries,the employer or recruiter may elect to purchase a web site subscription,which allows access to each candidate's resume and contact information.Furthermore, when an employer or recruiter subscribes to the web siteand accesses various candidate information, the employer or recruitermay also elect to engage recruiting services to assist in pursuing thecandidate.

The automatic match step 60 is designed to run continuously as new jobpostings and resumes are added to the searchable content database 46.The match step 60 is scalable, as in practice several servers can beoperated in parallel to perform this matching and e-mail notificationprocess. As the input information flow to the searchable contentdatabase 46 increases, additional servers can be added to handle the newload.

1. A computer-implemented method of managing employment data so as toprovide access to the employment data via the Internet, the methodcomprising the steps of: collecting employment data from informationavailable on the Internet, wherein the step of collecting includes, viacomputer software, continuously visiting web sites on the Internet, anddetermining if the content from the visited web sites relates toemployment data; formatting, parsing and storing the employment datainto a database, via computer software, including job title, joblocation, job description, employer name, and corresponding employmentdata URL; and automatically updating the employment data stored in thedatabase by revisiting the employment data websites on a continuousbasis, via computer software.
 2. The method of claim 1, whereinemployment data includes job openings, job postings, job listings, andrelated employment information.
 3. The method of claim 1, furthercomprising expanding the time between revisiting if the content has notchanged.
 4. A computer-implemented method of managing employment data soas to provide access to the employment data via the Internet, the methodcomprising the steps of: collecting employment data from the informationavailable on the Internet, wherein the step of collecting includes, viacomputer software, continuously visiting web sites on the Internet,examining content from the visited web sites, and determining if thecontent from the visited web sites relates to employment data;formatting, parsing and storing the employment data and correspondingURL into a database, via computer software; and automatically updatingthe employment data stored in the database, via computer software.
 5. Acomputer-implemented method of providing access to employment data viathe Internet, the method comprising the steps of: establishing validemployment data criteria; randomly visiting web sites on the Internet,via computer software; examining content from the visited web sites, viacomputer software; determining, via computer software, if the contentfrom the visited web sites meet the employment data criteria; storing,via computer software, the URL corresponding to the visited web sitesthat meet the employment data criteria and information relevant to thecontent of the visited web sites into a database; revisiting, viacomputer software, the web sites that meet the employment data criteriaon a periodic basis to determine whether the content has changed; andproviding access to employment data via the database.
 6. The method ofclaim 5, further comprising expanding the periodic revisiting time ifthe content has not changed.
 7. The method of claim 5, furthercomprising removing the URL from the data base of actively searched websites after determining the web site no longer meets the employment datacriteria.
 8. The method of claim 5, wherein the step of determining ifthe visited web sites meet the employment criteria is done throughconcept searching.
 9. The method of claim 8, wherein the step ofconcept-based searching includes using concept-based software.
 10. Acomputer-implemented method of managing employment data so as to provideaccess to the employment data via the Internet, the method comprisingthe steps of: collecting employment data from the Internet, wherein thestep of collecting includes, via computer software, continuouslyvisiting web sites on the Internet, examining content from the visitedweb sites, and determining if the content from the visited web sitesrelates to employment data; formatting, parsing and storing theemployment data into a database, via computer software; and providingaccess to employment data in a common format via the database.
 11. Themethod of claim 10, wherein the formatting and parsing the employmentdata is done by using formatting software.
 12. A computer-implementedmethod of managing employment data so as to provide access to theemployment data via the Internet, the method comprising the steps of:determining, via computer software, whether a web site containsemployment data; formatting, parsing and storing the employment data andcorresponding URL into a database, via computer software; revisiting anddetermining whether a previously visited web site has revised employmentdata, and formatting parsing and storing the revised employment data andrespective web site address into the database for each web site whichhas revised the employment data, via computer software; automaticallysearching, via computer software, the database for matching employmentdata; and contacting, via computer software, the employer representativeas to the matched employment data.
 13. The method of claim 12, whereinthe step of contacting includes providing the employer representativewith a portion of the matching employment data and offering all of thematching employment data upon the purchase of a subscription.
 14. Themethod of claim 12, further comprising providing the employerrepresentative the authority to search the database for matchingemployment data, providing a portion of the matching employment data andoffering all of employment data upon the purchase of a subscription. 15.The method of claim 12, further comprising the steps of confirming thata previously visited web site continues to contain employment data, andremoving the previously stored URL from the database of activelysearched web sites in the event the revisited web site no longercontains employment related data.
 16. The method of claim 12, furthercomprising the step of adjusting the period of revisiting based on thedegree to which the employment data has been revised.
 17. Acomputer-implemented method of providing employment data available viathe Internet, the method comprising the steps of: repetitively visitingweb sites on the Internet, via computer software; examining content fromthe visited web sites, via computer software; determining, via computersoftware, if the content from the visited web sites relates toemployment information; parsing employment information including jobtitle, job location, employer name, and corresponding URL, via computersoftware; storing the employment information into a database, viacomputer software; and providing employment information available fromthe database.
 18. The method of claim 17, wherein the step of visitingweb sites includes randomly visiting web sites on the Internet.
 19. Themethod of claim 17, wherein the step of visiting web sites includesrevisiting the web sites that relate to employment information on aperiodic basis to determine whether the content has changed.
 20. Themethod of claim 19, further comprising expanding the periodic revisitingtime if the content has not changed.
 21. A computer-implemented methodof providing employment data available via the Internet, the methodcomprising the steps of: searching, via computer software, the Internetfor employment data posted on the Internet; determining, via computersoftware, a web address for the employment data identified in thesearching step; maintaining, via computer software, an index databasefor the employment data identified in the searching step, the indexdatabase including the respective web address for the employment dataidentified in the searching step; routinely revisiting, via computersoftware, the web address to determine if information, regarding theemployment data posted at the web address, has changed, andautomatically updating the index database with any changed information;and providing employment information available from the database. 22.The method of claim 21, further comprising the steps of determining thekeywords for the employment data identified in the searching step, andmaintaining the index database to include the respective URL and thekeywords for the employment data identified in searching step.
 23. Themethod of claim 21, further comprising the step of: continuouslysearching the Internet for new information regarding employment dataposted on the Internet; and updating the index database with the newinformation.
 24. The method of claim 21, wherein the step of searchingthe Internet includes searching the Internet for employment data usingconcept searching.
 25. The method of claim 24, wherein the step ofconcept-based searching includes using concept-based search software.